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Record W3143475113 · doi:10.5194/egusphere-egu21-14795

Testing hypotheses of stress drop variations with hydraulic fracturing induced seismicity in the Horn River basin

2021· article· en· W3143475113 on OpenAlexaboutno aff
Adam Klinger, Joanna M. Holmgren, Maximilian J. Werner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilNatural Environment Research CouncilSight Research UK
KeywordsInduced seismicityMicroseismHydraulic fracturingBoreholeGeologySeismologyStructural basinStress (linguistics)Geotechnical engineeringGeomorphology

Abstract

fetched live from OpenAlex

Source parameters can help constrain the causes and mechanics of induced earthquakes. In particular, systematic variations of stress drops of fluid-injection induced seismicity have been interpreted in terms of the role of fluids, differences between tectonic and induced events, and self-similarity. The empirical basis for the variations, however, remains controversial. Here, we test three hypotheses about stress drops with observations of seismicity induced by hydraulic fracturing in the Horn River basin (Canada). First, stress drop is self-similar and independent of magnitude. Second, stress drop increases with distance from the point of fluid injection, which might be expected if in-situ effective stresses increase away from the point of fluid injection. Third, stress drops estimated with empirical Green’s functions (EGFs) are systematically larger than those estimated from direct fits to source models, which is expected if seismic waves attenuate in a frequency-dependent manner or experience site effects. We probe the hypotheses with a large microseismic dataset collected during hydraulic fracturing operations in the Horn River shale gas play in British Columbia. 90,000+ seismic events were recorded by three borehole geophone arrays with a moment magnitude range of -3 < Mw < 0.5. To calculate corner frequencies, we assume small, co-located seismic events can be approximated as EGFs, which effectively remove propagation and site effects from a larger target event. We target 34 Mw > 0 events and search for EGFs over a 100 m radius for each event, choosing only those EGFs that satisfy multiple quality criteria. This study builds on previous work that estimated stress drops from direct fitting of standard Brune source models and found systematic high frequency resonances recorded by the geophones. Of the 34 target events, we retrieve corner frequency and stress drop estimates for 22 events to test the three hypotheses. We observe that stress drop appears relatively constant over Mw , but the magnitude range (0 < Mw < 0.5) is currently too limited to draw strong conclusions. Second, stress drop appears to decrease, rather than increase, with distance from the point of injection (with a moderate Pearson’s correlation co-efficient of -0.5 ± 0.2); this could be caused by a direct hydraulic connection causing a reduction of in-situ effective normal stresses distal to the point of injection. Third, we observe no systematic difference between stress drops from direct source fits and EGF-based estimates, although stress drop uncertainties are large compared to standard earthquake source studies because of limited azimuthal coverage and high-frequency instrument resonances. These initial results do not support the systematic variations of stress drop for fluid-injection induced seismicity that have been observed in other datasets.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.875

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.220
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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